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Dwiza Riana
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Journal Medical Informatics Technology
Published by Asia Pacific Network for Sustainable Agriculture, Food and Energy
ISSN : 29887003     EISSN : 29887003     DOI : https://doi.org/10.37034/medinftech
Core Subject : Health, Science, Engineering,
Computer Science & IT Dentistry Engineering Medicine & Pharmacology Public Health
Journal Medical Informatics Technology publishes papers on innovative applications, development of new technologies and efficient solutions in Health Professions, Medicine, Neuroscience, Nursing, Dentistry, Immunology, Pharmacology, Toxicology, Psychology, Pharmaceutics, Medical Records, Disease Informatics, Medical Imaging and scientific research to improve knowledge and practice in the field of Medical.
Arjuna Subject : Teknik (semua kategori) - Teknik Biomedis
Articles 60 Documents
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Measuring User Acceptance Of ALODOKTER Application With Technology Acceptance Model To Enhance Health Service Quality Malau, Fransiscus Rolanda; Sakti, Ichtiar Akbar
Journal Medical Informatics Technology Volume 3 No. 3, September 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v3i3.47

Abstract

ALODOKTER is one quickly evolving application in the healthcare services sector. The purpose of this application is to help medical professionals carry out their jobs more effectively by giving the community rapid and easy access to healthcare services. This study aims to measure user acceptance of the ALODOKTER application using the Technology Acceptance Model (TAM) approach to improve the use and quality of health services. A survey method with a quantitative approach was employed to analyze perceived ease of use (PEU), perceived usefulness (PU), attitude towards use (ATU), behavioral intention to use (BIU), and actual use (AU) of the application. The study involved 41 respondents from various demographic backgrounds. Results show significant relationships between user perception variables, attitudes, and actual use. Correlation analysis revealed strong relationships between PEU, PU, and ATU, with a very strong correlation between ATU and BIU. Linear regression analysis indicated that BIU was the strongest predictor of actual use of the app (β = 1.066, p < 0.01), followed by PU (β = 0.628, p < 0.01). The regression model explained 38.7% of the variance in actual use. Cronbach's Alpha coefficients for all scales exceeded 0.9, indicating high reliability of the instruments used. This research suggests that ALODOKTER developers should focus on enhancing the perceived usefulness and ease of use of the application to increase acceptance and use. The study's limitations include a small sample size and reliance on self-reporting, suggesting the need for further research with larger samples and more diverse methods.
Optimization of Melanoma Skin Cancer Detection through Data Magnification, Filter Preprocessing, Image Enhancement, and Convolutional Techniques Fatimah Asmita Rani
Journal Medical Informatics Technology Volume 3 No. 3, September 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v3i3.48

Abstract

Melanoma skin cancer is one of the most aggressive forms of cancer, requiring early detection to improve patient outcomes. This study evaluates three image processing methods—Laplacian, Box Blur, and Edge Detection—used in melanoma detection, analyzing their performance using Mean Squared Error (MSE) and Structural Similarity Index (SSIM) metrics. Among these, Box Blur demonstrated the best overall performance with the lowest average MSE (104.16), indicating minimal distortion in the processed images. Additionally, it achieved the highest SSIM score (0.851), suggesting that it best preserved the structural integrity of the images, making it the most effective in maintaining both quality and important diagnostic details. In contrast, Edge Detection produced the highest MSE (108.02) and a negative SSIM score (-0.016), significantly distorting image structure and making it less suitable for melanoma detection. Laplacian, while moderate in performance, did not outperform Box Blur, with an MSE of 106.99 and an SSIM of 0.175. These results highlight Box Blur as the most reliable technique for melanoma image analysis, ensuring both clarity and structural preservation. By effectively enhancing diagnostic features and reducing errors, Box Blur offers a valuable tool for clinicians aiming to improve diagnostic accuracy in melanoma detection.
Optimisation of Image Morphology Operations with Enhancement and Convolution in Tomato Leaf Disease Symptom Recognition Indra, Muhamad
Journal Medical Informatics Technology Volume 3 No. 3, September 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v3i3.50

Abstract

Tomato (Solanum lycopersicum) is an important horticultural crop that is highly susceptible to various leaf diseases such as leaf spot, bacterial wilt, and fruit rot, which significantly reduce yield and quality. This study applies digital image processing techniques including pre-processing, morphology, enhancement, and convolution to improve the recognition of disease symptoms on tomato leaves. Pre-processing using grayscale conversion and median blur effectively reduces noise and sharpens essential details, while morphological operations (erosion and dilation) highlight structural features of infected areas. Enhancement techniques increase image contrast, making the distinction between healthy and diseased tissue more visible. Convolution methods with kernels such as Sobel and Gabor further emphasize edges and texture patterns of leaf lesions. Experimental results show that these methods improve pixel intensity distribution and enhance the visibility of disease symptoms, thereby increasing diagnostic accuracy. The integration of these techniques demonstrates the potential for early detection and classification of tomato leaf diseases, enabling more effective disease management and prevention of crop losses.
Optimizing Image Quality for Dog Skin Disease Diagnosis: Bacterial, Fungal, and Hypersensitivity Cases with MATLAB Puspitaningtyas, Mery Oktaviyanti; Na`am, Jufriadif
Journal Medical Informatics Technology Volume 3 No. 3, September 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v3i3.54

Abstract

Skin diseases in dogs, such as hypersensitive dermatitis, fungal infections, and bacterial dermatoses, present diverse clinical signs that complicate diagnosis in veterinary practice. This study employs MATLAB as an image-processing tool to enhance diagnostic accuracy through a structured pipeline. A dataset of 500 canine skin images obtained from Kaggle was processed using enlargement, histogram equalization, Gaussian filtering, and Sobel convolution. These methods improved image quality by enhancing contrast, reducing noise, and clarifying lesion boundaries. The experimental results demonstrate that the processed images allow veterinarians to more easily detect key diagnostic features, including changes in lesion texture, color, and shape. Enhanced visual clarity supports faster identification of disease patterns and reduces diagnostic ambiguity in clinical settings. This study highlights the potential of MATLAB-based image processing as an effective decision-support tool for veterinary dermatology, enabling quicker and more reliable treatment planning. Future work may integrate deep learning classification to further automate disease recognition.
Identification of Cell Images in Pap Smear Using GLCM and Classification Methods in Machine Learning Agustino, Rano; Fauziah, Prima Nanda
Journal Medical Informatics Technology Volume 3 No. 3, September 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v3i3.85

Abstract

Early detection of cervical cancer is critical for improving patient outcomes, and accurate classification of Pap smear images supports clinical decision-making. This study aimed to improve cervical cancer diagnosis by classifying Pap smear images using texture features. A dataset of 250 images across five classes underwent preprocessing including grayscale conversion and noise removal. Texture features such as contrast, dissimilarity, homogeneity, energy, correlation, and Angular Second Moment (ASM) were extracted using the Gray-Level Co-occurrence Matrix (GLCM). These features were then used to train and evaluate machine learning algorithms: Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), and Neural Networks (NN). The Decision Tree model achieved the highest accuracy of 95%, outperforming Neural Networks which reached 74%. Ensemble methods like RF and GB showed robust performance across classes. These results demonstrate the effectiveness of GLCM-based feature extraction combined with Decision Tree classification for accurate and reliable Pap smear image analysis. This approach offers valuable insights for enhancing clinical decision support in cervical cancer diagnosis.
Implementation of Image Processing Techniques for Viral Pneumonia Diagnosis Using Chest X-Ray Images Wardani, Yulita Ayu
Journal Medical Informatics Technology Volume 3 No. 4, December 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v3i4.52

Abstract

The COVID-19 pandemic has increased the need for rapid and accurate diagnostic methods for viral pneumonia diseases. This study employs an experimental approach to analyze the application of image processing techniques for viral pneumonia diagnosis using chest X-ray images. The dataset consists of 100 chest X-ray images obtained from a public Kaggle repository, including normal and viral pneumonia cases. The proposed methodology involves several main stages. Image preprocessing is performed through image enlargement using bilinear interpolation and noise reduction using a median filter to improve image quality. Morphological operations, including erosion and dilation, are applied to enhance lung structures and clarify anatomical contours. Image enhancement is conducted using histogram equalization to improve contrast between healthy and infected regions. Finally, convolution-based edge detection using the Sobel operator is applied to highlight structural boundaries relevant to diagnostic interpretation. This processing framework aims to enhance image clarity and feature visibility, thereby supporting more efficient and consistent analysis of chest X-ray images for viral pneumonia diagnosis.
Identification of Rotten Carrots Using Image Processing with Edge Detection and Convolution Techniques Syofian, Muhammad
Journal Medical Informatics Technology Volume 3 No. 4, December 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v3i4.53

Abstract

Carrot is one of the agricultural commodities with high nutritional value and a significant market demand. However, its quality can deteriorate due to various factors, one of which is rotting. Early detection of rotting carrots is crucial to prevent economic losses and maintain product quality. The main problem in identifying rotten carrots lies in the need for high precision and the time-consuming nature of manual methods. To address this issue, this research develops an automated method for detecting rotten carrots using image processing techniques. In this study, edge detection and convolution techniques are employed as the primary approaches in image analysis. Edge detection is used to recognize contours and boundaries in carrot images, while convolution techniques are applied to identify patterns of damage and texture differences between rotten and healthy carrots. The research findings indicate that this method is capable of detecting rotten carrots with high accuracy, making it reliable as a tool for sorting and quality assurance in carrot processing.
The Correlation Between Social Support And Self-Esteem Among Final-Year Students At Bhakti Husada University Indonesia Pranatha, Aria; Apriyanty, Vevi
Journal Medical Informatics Technology Volume 3 No. 4, December 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v3i4.109

Abstract

The World Health Organization (WHO) reports that low self-esteem is a prevalent mental health issue worldwide, affecting an estimated 121 million people, including 5.8% of men and 9.5% of women, with many cases occurring among individuals in their productive years—university students included. The objective of this study was to determine the relationship between social support and self-esteem among final-year nursing students at Bhakti Husada University Indonesia. This study employed a quantitative approach using a correlational descriptive method and a cross-sectional design. The population consisted of 178 final-year nursing students working on their theses in 2022, from which a sample of 113 was selected through proportionate stratified random sampling. The research instruments included a social support questionnaire and a self-esteem questionnaire, and the data were analyzed using the Spearman’s Rank correlation test. The bivariate analysis yielded a p-value of 0.000 and a correlation coefficient (rho) of 0.547, indicating a significant relationship between social support and self-esteem among final-year students. The findings of this study provide practical implications, particularly for students, educators, and academic institutions, by emphasizing the importance of strengthening social support systems to enhance students’ self-esteem, improve coping abilities, and support their academic completion. It is hoped that students will be able to build greater self-confidence, become more open, and develop supportive relationships with family, friends, and academic advisors while facing the challenges of completing their theses.
Comparison of Naive Bayes and Decision Tree Methods in Breast Cancer Classification Sulistyowati, Daning Nur; Hadianti, Sri; Mayangky, Nissa Almira
Journal Medical Informatics Technology Volume 3 No. 4, December 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v3i4.112

Abstract

The early diagnosis of breast cancer is a critical factor in improving recovery rates and reducing cancer-related mortality. This study aims to compare the performance of two widely used machine learning algorithms in medical data classification Naive Bayes and Decision Tree in detecting breast cancer using the Breast Cancer Wisconsin (Diagnostic) dataset. The dataset consists of 569 samples with 30 numerical features and one target label. The methodology includes data preprocessing, model training, and performance evaluation using six metrics: accuracy, precision, recall, F1-score, AUC, and MCC. Naive Bayes achieved higher performance, with 96.5% accuracy, 97.6% precision, 93.0% recall, 95.2% F1-score, 0.997 AUC, and 0.925 MCC, compared to Decision Tree with 93.9% accuracy, 90.9% precision, 93.0% recall, 92.0% F1-score, 0.936 AUC, and 0.87 MCC. Confusion matrix and ROC curve analyses support these results, particularly in minimizing classification errors. While Decision Tree offers better interpretability, Naive Bayes may be more suitable for early breast cancer detection under similar dataset conditions. Future studies could explore ensemble approaches to combine the strengths of both methods.
Enhancing the Quality of Digital Panoramic Radiographs with Median Filtering and Histogram Equalization Techniques Yusuf, Moh.; Novianty, Shella Indri; Maharani, Bella
Journal Medical Informatics Technology Volume 3 No. 4, December 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v3i4.114

Abstract

Digital panoramic radiographs often suffer from noise and reduced contrast, which can compromise diagnostic accuracy and treatment planning. Conventional enhancement methods rely on default Carestream software with manual adjustments, which may be inconsistent and time-consuming. This study aims to improve the quality of digital panoramic radiographs by applying Median Filtering (MF) to reduce noise and Histogram Equalization (HE) to enhance contrast using MATLAB R2022a. An analytical experimental design was conducted on 155 digital panoramic radiograph images, sampled from a total population of 254 images collected between July 2021 and July 2022 at the Dental Radiology Installation, Islamic Dental and Oral Education Hospital of Sultan Agung Semarang (RSIGMP-SA), using the Slovin formula. Radiograph files in DICOM format were converted to JPEG for analysis. Image quality was evaluated using Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR), and statistical significance was analyzed with the paired T-test in SPSS. The results indicated normal distribution for all parameters (p>0.05) and significant improvements in SNR and CNR after enhancement (T=-14.426 for SNR, T=-41.673 for CNR, p<0.05). These findings demonstrate that MF effectively enhances image sharpness, while HE improves contrast, resulting in clearer and more diagnostically reliable panoramic radiographs. The proposed approach provides a standardized and reproducible method for image quality improvement and offers potential support for clinical decision-making.

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